TY - JOUR
T1 - Time-resolved single-cell RNA-seq using metabolic RNA labelling
AU - Erhard, Florian
AU - Saliba, Antoine-Emmanuel
AU - Lusser, Alexandra
AU - Toussaint, Christophe
AU - Hennig, Thomas
AU - Prusty, Bhupesh K.
AU - Kirschenbaum, Daniel
AU - Abadie, Kathleen
AU - Miska, Eric A.
AU - Friedel, Caroline C.
AU - Amit, Ido
AU - Micura, Ronald
AU - Doelken, Lars
N1 - This work was supported by the European Research Council (ERC-2016-CoG 721016–HERPES and ERC-2022-CoG 101041177–DecipherHSV to L.D.), the Deutsche Forschungsgemeinschaft (DFG) to F.E. (ER 927/2-1) and C.C.F. (FR 2938/9-1) and the Amar Foundation to B.K.P. A.-E.S. is supported by the Bundesministerium für Bildung und Forschung (BMBF, HOPARL (COMPLS4-025)) and NIH NHGRI R01. F.E., A.-E.S. and L.D. are jointly supported by the DFG CRC1525 (453989101) and by the FOR-COVID (Bayerisches Staatsministerium für Wissenschaft und Kunst). E.A.M. is supported by a Wellcome Trust Senior Investigator award (219475/Z/19/Z) and CRUK awards (C13474 and A27826). I.A. is an Eden and Steven Romick Professorial Chair, supported by Merck KGaA, Darmstadt, Germany, the Chan Zuckerberg Initiative (CZI), the HHMI International Scholar award, the ERC Consolidator Grant (ERC-COG) 724471 HemTree2.0, an SCA award of the Wolfson Foundation and Family Charitable Trust, the Helen and Martin Kimmel award for innovative investigation, the NeuroMac DFG/Transregional Collaborative Research Center Grant. This work was supported by the Austrian Science Fund FWF (P31691 and F8011-B to R.M.; P33936 and F8009-B to A.L.). The Helmholtz Institute for RNA-based Infection Research (HIRI) supported this work with a seed grant through funds from the Bavarian Ministry of Economic Affairs and Media, Energy and Technology (grant allocation nos. 0703/68674/5/2017 and 0703/89374/3/2017).
PY - 2022/12
Y1 - 2022/12
N2 - Single-cell RNA genomics technologies are revolutionizing biomedical science by profiling single cells with unprecedented resolution, providing fundamental insights into the role of different cellular states and intercellular heterogeneity in health and disease. The combination of single-cell RNA sequencing (scRNA-seq) with metabolic RNA labelling approaches now enables time-resolved monitoring of transcriptional responses for thousands of genes in thousands of individual cells in parallel. This facilitates and accelerates direct characterization of the temporal dimension of biological processes, which has been largely missing in current data. In this Primer, we provide an overview of the various metabolic RNA labelling approaches and their combination with currently available scRNA-seq and multi-omics platforms. We summarize the main challenges in the design of such experiments and discuss the various applications of time-resolved scRNA-seq in vitro and in vivo. We outline the computational tools and challenges to the analyses of the temporal dynamics of transcriptional responses at the single-cell level. We discuss the prospect of integrating data obtained by the respective time-resolved scRNA-seq approaches with complementary methods to elucidate gene regulatory networks that underlie molecular mechanisms. Finally, we discuss open questions and challenges in the field and give our thoughts for future development and applications.
AB - Single-cell RNA genomics technologies are revolutionizing biomedical science by profiling single cells with unprecedented resolution, providing fundamental insights into the role of different cellular states and intercellular heterogeneity in health and disease. The combination of single-cell RNA sequencing (scRNA-seq) with metabolic RNA labelling approaches now enables time-resolved monitoring of transcriptional responses for thousands of genes in thousands of individual cells in parallel. This facilitates and accelerates direct characterization of the temporal dimension of biological processes, which has been largely missing in current data. In this Primer, we provide an overview of the various metabolic RNA labelling approaches and their combination with currently available scRNA-seq and multi-omics platforms. We summarize the main challenges in the design of such experiments and discuss the various applications of time-resolved scRNA-seq in vitro and in vivo. We outline the computational tools and challenges to the analyses of the temporal dynamics of transcriptional responses at the single-cell level. We discuss the prospect of integrating data obtained by the respective time-resolved scRNA-seq approaches with complementary methods to elucidate gene regulatory networks that underlie molecular mechanisms. Finally, we discuss open questions and challenges in the field and give our thoughts for future development and applications.
UR - https://www.scopus.com/pages/publications/85139058799
U2 - 10.1038/s43586-022-00157-z
DO - 10.1038/s43586-022-00157-z
M3 - Article
SN - 2662-8449
VL - 2
JO - Nature Reviews Methods Primers
JF - Nature Reviews Methods Primers
IS - 1
M1 - 77
ER -